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In set-up two figure 4 the closet has Space, Effort, Shape, and Relationship. Together these a corner in it, and the garments are placed with more components constitute a qualitative language for describing space between them.

Proceedings of Design and Semantics of Form and Movement DeSForM 2009

We focused on the Effort factors since these are strong indications of human expression. For The participants acted out scenarios in the two different example, Quick movements point towards a hasty mindset closet set-ups.

For example a scenario was: you are in a of a person, whereas a Sustained movement relates to hurry, get your tennis clothes. The scenarios used in the a relaxed mindset of a person. Every movement quality is a continuum from the participants. In LMA these scenarios were analyzed and coded in terms of Effort elements are seen as the smallest units needed in movement qualities by two people trained during a one- describing an observed movement. In LMA the combination of two movement movement qualities, while closet set-up two elicited qualities from the Effort factors is called a State.

It was too early to find why this difference occurred. A conclusion for 2. This notion is deeply connected to LMA through A second conclusion from the test was to focus on the for example the Space Effort [23].

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To find out how the Space, Flow and Time Efforts. The Weigh Effort was not physical design of a walk in closet would change the relevant for the proposed scenarios.

The goal of this experiment was to find balance qualities between the amount of movement which could be To interpret the human movement qualities of the user elicited from participants while maintaining efficiency on a dimension which is useful in everyday practice, we in choosing garments. Set-up one within the test. The closet is placed in a line parallel Fig.

The second closet set-up used in the test. The closet is to the walking area and the garments are placed concentrated. All the scenarios had as main context Direct 1 2 Space a selection of clothes for different occasions, but the Indirect 3 4 feeling for example stressed or relaxed was varied.

The four combinations possible in the Awake state.

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Tonight you will have a drink with a friend. You have the time to prepare your clothes, were unique enough to distinguish different behaviors you are very relaxed. You already know what to wear, of the user. Take your favorite party clothes, in the vertical space in the walk-in closet we asked you have all the time in the world.

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From 2. You had a long day the analysis of the sensor data it became apparent that at school, and you just returned home. You are going to party tonight and you movement analysis: micro analysis of the movements in will be picked up in 10 minutes. You will take your the kinesphere of the user personal space surrounding favorite party clothes tonight, hurry!

You had movements in the space. Last week you and your friends decided to go to Space and Time Efforts to reduce complexity and partying tonight. You are going to your walk-in closet because qualitative observation by the LMA specialist of and take your time to choose clothes.

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It is a special the video recording showed that and sensor recordings occasion to be together with so many friends, so take were most distinguishable for these two Efforts. A combination of the Space and Time effort can be 4. Today you described as the Awake State.

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Because every Effort worked at your part-time job, it was a long day but consists of two extremes it possible to create four you still feel full of energy. You call your friend in different combinations of the Awake State figure 5. Amsterdam and ask if she wants to party tonight. You will have to leave in 10 minutes to catch the train! You want special clothes for this special occasion, so you decide not to wear your favorite clothes. Choose a set of clothes, but hurry!

We used the simpler Neural Gas algorithm to analyze the Fig. The prototype set-up: in the shelves six passive infrared abstraction of movement data from our sensors, in a sensors.

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On the floor 12 sensor mats which can switch between way similar to [20] and to find data structure which on and off state. In the shelves we placed six passive infrared sensors To enable the neural gas algorithm to find relevant which measure activity, which we linked back as micro structures in the input data we need to record data movement. An illustration of the set-up can be found which has a relation with the movement qualities that in figure 7. First these recordings function as This raw data can be considered as physical signals, but training data for the algorithm to divide them on classes the parameters which can be calculated from this data and secondly the algorithm uses these classes to cluster as for example speed can also be considered as a layer the new coming data.

The sensors were connected through three The layered conceptual framework for expressive Arduino microcontrollers [26] to a PC. Calculations gesture applications of Camurri et al. We used this parameters are calculated to create specific motion framework as guidance to process raw data to a cues.

We implemented this, for example, by calculating meaningful interpretation about the state of the user the percentage of movement with high acceleration on a dimension useful in everyday practice. In our system we use the 3rd layer of the framework as a 3. These training sets can be composed from layer is the acquisition of physical signals. Our approach any combination of parameters from the first and to interpret human physical movement was to use second layer.

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We found out that the quality of the relatively simple sensors, which placed on appropriate interpretation by the neural gas network depends on positions in the closet are able to capture unique how the training sets are composed. To capture macro movement we placed 12 the 4th layer. The neural gas algorithm is able to find sensor mats mm x mm in the floor which are structures from the training sets after a training session Layer 2 Layer 3 Layer 1 Layer 4 Low-level features and Mid-level features Physical Signals Concepts and structures statistical parameters and maps Fig.

The layered conceptual framework for expressive gesture applications, adapted from Camurri et al. When the sensor sources are for different user test participants. Some participants chosen well, the clustering information can be related used more Indirect movement throughout the entire back to Laban movement qualities. The algorithm was scenario, while others used more Direct movement.

A possible explanation for this outcome is that the system was able to interpret the difference in 4 Experiment to test the interpretation specific movement styles of the participants. Another of movements explanation is that the initial training session by the two With a user test we were aiming to find out if the persons was not personalized enough and worked for prototype we developed in the preceding iterative some participants, but failed for those with a different process was indeed able to interpret the human movement style.

Further study is needed to be able to movement qualities of the Awake state. In figure 8 the explain this phenomenon. The system was trained with training data compiled 5 Towards a research platform from 2 people who were trained in LMA during a one The experiment described in previous section raised day LMA workshop, the same training set was used many questions and it is difficult to draw strong during the whole test. The test consisted of 10 male conclusions. However, the process leading to the walk-in participants, chosen using convenience sampling, who closet prototype and the experiment lead us to believe acted out the scenarios to elicit different extremes of that further research can unveil interesting results.

The We have chosen to create a research platform which scenarios were read to the participants, after which the will enable us to conduct further research on the participants were asked to act out the scenario as good as integration of the fields of LMA, neural learning and possible.

Video was recorded of the participants, while the product design. Such a platform should communicate the interpretation data of the algorithm was also recorded. Therefore, the embodiment of the closet needed a drastic change, and was designed to be perceived as intelligent, and communicate the nature of the closet through the physical form. The shelves of the closet communicate this through the body line, adaptivity, complexity, dynamics, and cleanness of form. These aspects were influenced by the movements made by the users, resulting in the final prototype which is pictured in figure 2.